// Loss returns the weighted exponential loss.
// It determines which samples are positive vs. negative
// by checking the sign of the element in the expected
// vector.
func (w *WeightedExpLoss) Loss(actual autofunc.Result, expected linalg.Vector) autofunc.Result {
expVar := &autofunc.Variable{Vector: expected.Copy().Scale(-1)}
dots := autofunc.Mul(actual, expVar)
exps := autofunc.Exp{}.Apply(dots)
weightVec := &autofunc.Variable{Vector: make(linalg.Vector, len(expected))}
for i, x := range expected {
if x > 0 {
weightVec.Vector[i] = w.PosWeight
} else {
weightVec.Vector[i] = 1
}
}
return autofunc.SumAll(autofunc.Mul(exps, weightVec))
}
// Loss returns the squared magnitude of the difference
// between actual and expected.
func (_ SquareLoss) Loss(actual autofunc.Result, expected linalg.Vector) autofunc.Result {
expVar := &autofunc.Variable{Vector: expected.Copy().Scale(-1)}
return autofunc.SumAll(autofunc.Square(autofunc.Add(actual, expVar)))
}
// Loss returns the exponential loss, as given by
// exp(-actual*expected).
func (_ ExpLoss) Loss(actual autofunc.Result, expected linalg.Vector) autofunc.Result {
expVar := &autofunc.Variable{Vector: expected.Copy().Scale(-1)}
dots := autofunc.Mul(actual, expVar)
exps := autofunc.Exp{}.Apply(dots)
return autofunc.SumAll(exps)
}
func (_ MeanSquaredCost) CostR(v autofunc.RVector, a linalg.Vector,
x autofunc.RResult) autofunc.RResult {
aVar := &autofunc.Variable{a.Copy().Scale(-1)}
aVarR := autofunc.NewRVariable(aVar, v)
return autofunc.SquaredNorm{}.ApplyR(v, autofunc.AddR(aVarR, x))
}
